Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition--cognitively bounded rational analysis--that sharpens the predictive acuity of general, integrated…

External noise methods and observer models have been widely used to characterize the intrinsic perceptual limitations of human observers and changes of the perceptual limitations associated with cognitive, developmental, and disease processes by highlighting the variance of internal representations. The authors conducted a comprehensive review of…

The role of occlusion configurations in binocular vision was studied in 4 experiments with 10 adult observers. Results reveal that occlusion relationships are sensed during the earliest stages of binocular processing. A simple theoretical framework that unifies fusion, stereopsis, and occlusion is advanced. (SLD)

A theory of visual information acquisition and visual memory is described that conjoins two models that have been used to describe low-level perceptual and higher level cognitive processes. Six experiments with 21 adult observers generally support the theory, although some weakness is discussed. (SLD)